class_docstr: Feature loader that load external features store in certain format. 
  Currently support pickle, npy and npz format.
docstr: Convert sample type to float32.
  Integers will be scaled to [-1, 1] in float32.
docstr_tok: ["Convert","sample","type","to","float32",".","Integers","will","be","scaled","to","[","-1",",","1","]","in","float32","."]
code: def _convert_samples_to_float32(samples: np.ndarray) -> np.ndarray:
      """Convert sample type to float32.
      Integers will be scaled to [-1, 1] in float32.
      """
      float32_samples = samples.astype("float32")
      if samples.dtype in np.sctypes["int"]:
          bits = np.iinfo(samples.dtype).bits
          float32_samples *= 1.0 / 2 ** (bits - 1)
      elif samples.dtype in np.sctypes["float"]:
          pass
      else:
          raise TypeError(
              "Unsupported sample type: %s." % samples.dtype
          )
      return float32_samples
  
file_path: NeMo/nemo/collections/asr/parts/preprocessing/feature_loader.py
imports: from typing import Optional
  import numpy
  import torch
label_desc: Use image node_id 3 for calling the ExternalFeatureLoader obj's underlying member method code with example usage: obj._convert_samples_to_float32(samples) and returns: float32_samples